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Multicasting does not work well for video applications such as video on demand, where individual users request video content at discrete and unpredictable moments. In this paper, we study a concurrent video multicast orchestration solution for the caching-assisted and centrally controlled mobile networks. The proposed solution strives to obtain rapid video prefetch and effective traffic reduction. By rapid video prefetch, user devices can cache required contents in advance, which can effectively improve the streaming fluency in unsteady wireless environments, especially the highly mobile vehicular environment. We introduce a maximum-rate concurrent multicast (MRCM) problem whose solution is intended to maximize the total video delivery rate by a concurrent multicast manner, and prove that the MRCM problem is NP-hard. Then, we propose a dynamic concurrent multicast tree construction algorithm and a global concurrent multicast tree optimization algorithm to approximately solve the MRCM problem in polynomial time. We also introduce a cache competition mechanism that strives to maximize the total cache hit rate when the cache space is insufficient. Performance evaluations show that compared with the existing cache-assisted multicast delivery solutions, the proposed solution significantly reduces the video delivery duration and the traffic stress of backhaul links. [ABSTRACT FROM AUTHOR]

Conversational image search, a revolutionary search mode, is able to interactively induce the user response to clarify their intents step by step. Several efforts have been dedicated to the conversation part, namely automatically asking the right question at the right time for user preference elicitation, while few studies focus on the image search part given the well-prepared conversational query. In this paper, we work towards conversational image search, which is much difficult compared to the traditional image search task, due to the following challenges: 1) understanding complex user intents from a multimodal conversational query; 2) utilizing multiform knowledge associated images from a memory network; and 3) enhancing the image representation with distilled knowledge. To address these problems, in this paper, we present a novel contextuaL imAge seaRch sCHeme (LARCH for short), consisting of three components. In the first component, we design a multimodal hierarchical graph-based neural network, which learns the conversational query embedding for better user intent understanding. As to the second one, we devise a multi-form knowledge embedding memory network to unify heterogeneous knowledge structures into a homogeneous base that greatly facilitates relevant knowledge retrieval. In the third component, we learn the knowledge-enhanced image representation via a novel gated neural network, which selects the useful knowledge from retrieved relevant one. Extensive experiments have shown that our LARCH yields significant performance over an extended benchmark dataset. As a side contribution, we have released the data, codes, and parameter settings to facilitate other researchers in the conversational image search community. [ABSTRACT FROM AUTHOR]

Video moment localization, as an important branch of video content analysis, has attracted extensive attention in recent years. However, it is still in its infancy due to the following challenges: cross-modal semantic alignment and localization efficiency. To address these impediments, we present a cross-modal semantic alignment network. To be specific, we first design a video encoder to generate moment candidates, learn their representations, as well as model their semantic relevance. Meanwhile, we design a query encoder for diverse query intention understanding. Thereafter, we introduce a multi-granularity interaction module to deeply explore the semantic correlation between multi-modalities. Thereby, we can effectively complete target moment localization via sufficient cross-modal semantic understanding. Moreover, we introduce a semantic pruning strategy to reduce cross-modal retrieval overhead, improving localization efficiency. Experimental results on two benchmark datasets have justified the superiority of our model over several state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

Monitoring packet losses at links is beneficial to network management and service quality improvement. Software-defined networking can conveniently obtain flow statistics. However, the packet loss rate of a link or path cannot be directly measured by acquiring the statistics of an ongoing flow. In addition, monitoring the packet loss directly based on statistics inquiries brings considerable extra overhead to the controller. In this paper, we propose a two-way link-level packet loss monitoring solution for the software-defined networks. We propose a packet loss probe structure consisting of distributed rings. The proposed probe structure contains every monitored directed link once and only once, thereby avoiding the mutual interference between different probe flows and thereby improves probe accuracy. We further study two optimization problems of the ring-based packet loss probe structure. The two problems build probe structures, with minimal maximum ring delay, for all the network links and selected network links. The two optimization problems are very complex, and we approximately solve them in polynomial time. A packet loss positioning algorithm, based on flow statistic inquiries and the symmetry design of probe rings, is also proposed. Our proposed solution can effectively monitor link-level packet loss but brings low and controllable overhead to the controller. [ABSTRACT FROM AUTHOR]

This paper presents a hierarchical controller for an autonomous vehicle to track a reference path in the presence of uncertainties in both tire–road condition and external disturbance. The hierarchical control architecture consists of three layers: high, low, and intermediate levels. The upper-layer module deals with the vehicle motion control objective, which generates the desired longitudinal/lateral forces and yaw moment. The low-level module handles the braking control for each wheel based on the wheel slip dynamics. The intermediate-level controller generates the longitudinal slip reference for the low-level brake control module and the front-wheel steering angles. To cope with the unknown and nonuniform road condition parameters appearing in the actuator models, an adaptive law is designed for each wheel, and the convergence of the adaptive parameters is guaranteed under a certain persistency-of-excitation condition. The stability of the integrated control system is analyzed by utilizing a Lyapunov function approach. Simulation results are included to illustrate the proposed control scheme. [ABSTRACT FROM PUBLISHER]